An alternating minimization algorithm for Factor Analysis
Optimization and Control
2018-06-13 v1
Abstract
The problem of decomposing a given covariance matrix as the sum of a positive semi-definite matrix of given rank and a positive semi-definite diagonal matrix, is considered. We present a projection-type algorithm to address this problem. This algorithm appears to perform extremely well and is extremely fast even when the given covariance matrix has a very large dimension. The effectiveness of the algorithm is assessed through simulation studies and by applications to three real datasets that are considered as benchmark for the problem. A local convergence analysis of the algorithm is also presented.
Cite
@article{arxiv.1806.04433,
title = {An alternating minimization algorithm for Factor Analysis},
author = {Valentina Ciccone and Augusto Ferrante and Mattia Zorzi},
journal= {arXiv preprint arXiv:1806.04433},
year = {2018}
}